SOTAVerified

Denoising

Denoising is a task in image processing and computer vision that aims to remove or reduce noise from an image. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. The goal of denoising is to recover the original image, which is considered to be noise-free, from a noisy observation.

( Image credit: Beyond a Gaussian Denoiser )

Papers

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Showing 3101–3125 of 7282 papers

TitleStatusHype
Deep Nonparametric Convexified Filtering for Computational Photography, Image Synthesis and Adversarial Defense—0
CCSPNet-Joint: Efficient Joint Training Method for Traffic Sign Detection Under Extreme ConditionsCode1
Introducing Shape Prior Module in Diffusion Model for Medical Image Segmentation—0
CleanUNet 2: A Hybrid Speech Denoising Model on Waveform and Spectrogram—0
Restoring Snow-Degraded Single Images With Wavelet in Vision TransformerCode0
Predicting the Radiation Field of Molecular Clouds using Denoising Diffusion Probabilistic Models—0
Discrete Denoising Diffusion Approach to Integer FactorizationCode0
HAT: Hybrid Attention Transformer for Image RestorationCode3
Diff-Privacy: Diffusion-based Face Privacy Protection—0
Anatomy Completor: A Multi-class Completion Framework for 3D Anatomy ReconstructionCode1
Multi-view Self-supervised Disentanglement for General Image DenoisingCode1
Prefix-diffusion: A Lightweight Diffusion Model for Diverse Image Captioning—0
Effective Real Image Editing with Accelerated Iterative Diffusion Inversion—0
DeNoising-MOT: Towards Multiple Object Tracking with Severe Occlusions—0
3D Denoisers are Good 2D Teachers: Molecular Pretraining via Denoising and Cross-Modal Distillation—0
From Text to Mask: Localizing Entities Using the Attention of Text-to-Image Diffusion ModelsCode0
MS-UNet-v2: Adaptive Denoising Method and Training Strategy for Medical Image Segmentation with Small Training Data—0
Data-Adaptive Graph Framelets with Generalized Vanishing Moments for Graph Signal ProcessingCode0
Underwater Image Enhancement by Transformer-based Diffusion Model with Non-uniform Sampling for Skip StrategyCode1
Reuse and Diffuse: Iterative Denoising for Text-to-Video GenerationCode1
Efficient Training for Visual Tracking with Deformable Transformer—0
Diffusion Model is Secretly a Training-free Open Vocabulary Semantic SegmenterCode1
Diffusion-EDFs: Bi-equivariant Denoising Generative Modeling on SE(3) for Visual Robotic ManipulationCode1
Bring the Noise: Introducing Noise Robustness to Pretrained Automatic Speech Recognition—0
Diffusion Generative Inverse Design—0
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All datasetsDarmstadt Noise DatasetDIV2KDNDiris

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81—Unverified
2Pixel-shuffling DownsamplingPSNR38.4—Unverified
3TWSCPSNR37.93—Unverified
4CBDNet(Syn)PSNR37.57—Unverified
5MCWNNMPSNR37.38—Unverified
6Han et alPSNR35.95—Unverified
7FFDNetPSNR34.4—Unverified
8TNRDPSNR33.65—Unverified
9CDnCNN-BPSNR32.43—Unverified
10NLRNPSNR30.8—Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92—Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64—Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61—Unverified